Brain tumour segmentation with incomplete imaging data

• Published in: Brain communications Vol. 5; no. 2; p. fcad118
• Main Authors: Ruffle, James K, Mohinta, Samia, Gray, Robert, Hyare, Harpreet, Nachev, Parashkev
• Format: Journal Article
• Language: English
• Published: US Oxford University Press 2023
• Subjects: Original; tumour segmentation; neuroradiology; deep learning; magnetic resonance imaging; artificial intelligence
• ISSN: 2632-1297; 2632-1297
• DOI: 10.1093/braincomms/fcad118

Abstract
Progress in neuro-oncology is increasingly recognized to be obstructed by the marked heterogeneity—genetic, pathological, and clinical—of brain tumours. If the treatment susceptibilities and outcomes of individual patients differ widely, determined by the interactions of many multimodal characteristics, then large-scale, fully-inclusive, richly phenotyped data—including imaging—will be needed to predict them at the individual level. Such data can realistically be acquired only in the routine clinical stream, where its quality is inevitably degraded by the constraints of real-world clinical care. Although contemporary machine learning could theoretically provide a solution to this task, especially in the domain of imaging, its ability to cope with realistic, incomplete, low-quality data is yet to be determined. In the largest and most comprehensive study of its kind, applying state-of-the-art brain tumour segmentation models to large scale, multi-site MRI data of 1251 individuals, here we quantify the comparative fidelity of automated segmentation models drawn from MR data replicating the various levels of completeness observed in real life. We demonstrate that models trained on incomplete data can segment lesions very well, often equivalently to those trained on the full completement of images, exhibiting Dice coefficients of 0.907 (single sequence) to 0.945 (complete set) for whole tumours and 0.701 (single sequence) to 0.891 (complete set) for component tissue types. This finding opens the door both to the application of segmentation models to large-scale historical data, for the purpose of building treatment and outcome predictive models, and their application to real-world clinical care. We further ascertain that segmentation models can accurately detect enhancing tumour in the absence of contrast-enhancing imaging, quantifying the burden of enhancing tumour with an R2 > 0.97, varying negligibly with lesion morphology. Such models can quantify enhancing tumour without the administration of intravenous contrast, inviting a revision of the notion of tumour enhancement if the same information can be extracted without contrast-enhanced imaging. Our analysis includes validation on a heterogeneous, real-world 50 patient sample of brain tumour imaging acquired over the last 15 years at our tertiary centre, demonstrating maintained accuracy even on non-isotropic MRI acquisitions, or even on complex post-operative imaging with tumour recurrence. This work substantially extends the translational opportunity for quantitative analysis to clinical situations where the full complement of sequences is not available and potentially enables the characterization of contrast-enhanced regions where contrast administration is infeasible or undesirable.
Brain tumour segmentation models with incomplete sets of MRI sequences—common in clinical practice—still delineate lesions well and even identify enhancing tumour without post-contrast imaging. The observed marginal benefits of additional MR sequences—especially contrast-enhanced—suggest that the costs and risks of imaging ordinarily sufficient to permit high-definition quantitative analysis may be reducible.
Graphical Abstract
Graphical Abstract